1. Field
Embodiments relate to an object pose recognition apparatus and an object pose recognition method using the same, and more particularly, to an object pose recognition apparatus to recognize the pose of an object from image data of the object input via a camera, and an object pose recognition method using the same.
2. Description of the Related Art
Generally, object recognition in a conventional computer vision field has been developed into a scheme to recognize objects registered through a feature extraction algorithm and a recognizer and then to determine if the registered objects are not present in incoming images, after constructing database of objects to be recognized and registering the objects through learning processes.
Since the use of a 3-dimensional (3D) Computer Aided Design (CAD) model during the construction of database of objects requires human input, practical application thereof to robot services is not possible. Recently, although a method to automatically extract and register the features of objects has been developed, limitations are still present in terms of robustness against environmental variations.
Environmental variation, with respect to object recognition, may be divided into a photometric invariance generated due to illumination variation or noise and a geometric invariance related to variation in the angle of a camera or the distance from an object. Invariance is important because, for robot services, users have no choice but to register objects having different types and features used in their homes in aspects of privacy protection. That is, it is impractical that engineers obtain a desired object list from users and resister objects. Since it is cumbersome to collect many images corresponding to variations of each of objects to learn objects, coping with many variations of an object and maintaining stable object recognition through one-time object registration are very important.
However, a conventional object pose recognition apparatus exhibits limited robustness against various environmental variations.